Data Visualization for Business Analytics


Systems and Components, Type of tasks and interactions and the most relevant data visualization techniques for multivariate data, geospatial data, hierarchical data, and, in general, time dependent data.


General characterization





Responsible teacher

João Carlos Gomes Moura Pires


Weekly - Available soon

Total - Available soon

Teaching language





Interactive Data Visualization: Foundations, Techniques, and Applications, Second Edition. Matthew O. Ward, Georges Grinstein, Daniel Keim, 2015, ISBN 9781482257373

Visualization Analysis & Design, Tamara Munzner, 2015, ISBN: 9781466508910

ISBN (e-Book): 9781498707763

Teaching method

Lectures will cover the fundamental topics of the course, including some time for questions and discussion, and will be illustrated with demos that students may replicate afterwards on their own. The first week will be focused on the first part of the syllabus and address the fundamental concepts and principles. The next 3 weeks will be devoted to explaining and training DV techniques for Multivariate Data, Geospatial Data and for Time-Oriented Data. The last two weeks will overview techniques for other types of data and systematically address the interactivity (roles and techniques) in DV and how to evaluate DV solutions / systems / techniques. Real datasets will be provided to students and will be used systematically as examples and training scenarios. Students are expected to practice and solve the proposed exercises autonomously, but part of the contact time will be devoted to discussing any practical problems they were unable to solve on their own.

Evaluation method

The evaluation of this curricular unit will consist of 2 quizzes (15% each) a practical team work assignment (30%) and the remaining 40% will be assessed in a final exam. 

Subject matter

(1) Introduction to Data Visualization: What is DV? What is the main Goal of DV? What is the fundamental idea of DV? Data Foundations. Some of the important aspects of Human Perception and Information Processing for DV. The Visual Variables. (

2) Visualization Techniques for: Multivariate Data; Geospatial Data; Time-Oriented Data; other types of data (overview).

(3) Interaction Concepts and Techniques: Interaction Operators, Operands and Spaces.

(4) Designing Effective Visualizations. Methodologies for Comparing and Evaluating Visualization Techniques.

The hands on and experimentation will be carried out using Tableau software (free and academic licenses) and eventually python libraries 


Programs where the course is taught: